Building internal capacity in pragmatic trials: a workshop for program scientists at the US National Cancer Institute
Bibliographic record
Abstract
BACKGROUND: Building capacity in research funding organizations to support the conduct of pragmatic clinical trials is an essential component of advancing biomedical and public health research. To date, efforts to increase the ability to design and carry out pragmatic trials have largely focused on training researchers. To complement these efforts, we developed an interactive workshop tailored to meet the roles and responsibilities of program scientists at the National Cancer Institute-the leading cancer research funding agency in the USA. The objectives of the workshop were to improve the understanding of pragmatic trials and enhance the capacity to distinguish between elements that make a trial more pragmatic or more explanatory among key programmatic staff. To our knowledge, this is the first reported description of such a workshop. MAIN BODY: The workshop was developed to meet the needs of program scientists as researchers and stewards of research funds, which often includes promoting scientific initiatives, advising prospective applicants, collaborating with grantees, and creating training programs. The workshop consisted of presentations from researchers with expertise in the design and interpretation of trials across the explanatory-pragmatic continuum. Presentations were followed by interactive, small-group exercises to solidify participants' understanding of the purpose and conduct of these trials, which were tailored to attendees' areas of expertise across the cancer control continuum and designed to reflect their scope of work as program scientists at NCI. A total of 29 program scientists from the Division of Cancer Control and Population Sciences and the Division of Cancer Prevention participated; 19 completed a post-workshop evaluation. Attendees were very enthusiastic about the workshop: they reported improved knowledge, significant relevance of the material to their work, and increased interest in pragmatic trials across the cancer control continuum. CONCLUSION: Training program scientists at major biomedical research agencies who are responsible for developing funding opportunities and advising grantees is essential for increasing the quality and quantity of pragmatic trials. Together with workshops for other target audiences (e.g., academic researchers), this approach has the potential to shape the future of pragmatic trials and continue to generate more and better actionable evidence to guide decisions that are of critical importance to health care practitioners, policymakers, and patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.142 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.009 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".